Physical articles are often marked for positive visual identification. Various forms of Optical Character Recognition (OCR) have been developed to enable computer controlled equipment to identify many different marking strategies. Bar codes, both one dimensional and two dimensional (QR codes) are examples of marking schemes which are easy for machines to recognize, but are difficult for humans to decipher. Other marking methods are easier for humans and harder for machines to identify. One particular marking scheme which has been inadequately addressed is described here, along with previous approaches and their shortcomings. A particularly successful and flexible OCR method involves machine learning techniques such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs).
A bigram constructed of two independent sets of glyphs can be used to uniquely mark items for identification. The most common example is that of a deck of playing cards. Each card is identified by a glyph from the set of ranks along with a glyph from the set of suits. Without both pieces of information the card cannot be uniquely identified. Additional decoration may be present which can aid proper identification, such as the common practice of repeating the suit glyph in a pattern across the face of a card, but this scheme breaks down for the court (face) cards. Such additional information also requires the entire face of each card be visible for processing. The remainder of the description and specification will reference the common playing card deck as it is a) so widely known, and b) directly applicable to several of the embodiments disclosed.
The accompanying Information Disclosure Statement includes an extensive list of relevant prior art references which show the approaches others have attempted to perform the task of identifying common playing cards within a digital image or video. The limitations each has imposed upon the problem in order to have any degree of success prevents any from functioning reliably within the context of a player holding a hand of cards in-hand. Several examples are provided here, along with a description of their shortcomings.
The gaming industry has inspired the most innovation regarding the recognition and identification of cards, primarily due to casinos' desire to combat cheaters and card counters. A number of card shoes and shufflers have been developed which scan cards as they are distributed to players, the dealer, and the board. All of these devices rely upon their tightly controlled mechanism to aid their recognition efforts. Even so, most prior art relies on additional markings printed on the cards, such as bar codes, for identification. Very few do more than mention briefly that an OCR algorithm, or an ANN could be used with existing card markings.
In U.S. Pat. No. 5,722,893 Hill does describe in detail the use of an ANN to classify the individual indicia printed on a playing card. His device, like most of the others, is a card shoe which scans cards individually as they travel through. Among the sensor technologies he lists specifically are Charge Coupled Devices (CCDs) and infrared cameras. His approach has several limitations which are shared by this class of prior art.
As previously mentioned, shoes and shufflers impose constraints which greatly simplify the process of card identification through an OCR utilizing an ANN. They dictate the distance from and orientation to the sensor with a small maximum allowable error tolerance. This allows for near perfect focus of the optical lens assembly. It also provides the location of each glyph with a minimum of error. The orientation of each glyph is fixed and known. Do to the opaque enclosure, any light source may be chosen for even illumination without requiring consideration of its effects outside the enclosure. Each card is processed individually so there is no need to identify which rank glyph is associated with which suit glyph.
A second, more advanced class of prior art is found primarily in scholarly articles. These approaches utilize a camera viewing playing cards laid out upon a table. Many restrict the location and orientation of the camera to the table. This class of prior art addresses more difficult problems than those operating within shufflers and shoes. The cards are allowed to rotate and translate across the plane of the tabletop. Lighting conditions may be inconsistent frame-to-frame or across the surface of the table (although some approaches prohibit this variable). With very few exceptions, all of these approaches requires that each card is cleanly surrounded by an even background. Many rely on accurately detecting one or more straight edges of each card to properly function, which is not often possible with cards held in-hand. No overlap between cards is tolerated, and background clutter is poorly handled. Most also require a clear view of the entirety of each card; objects in the foreground which obscure a card face will prevent operation or cause erroneous identification. While most handle rotation within the plane, none are equipped to process cards rotated along any other axis or arbitrary vector. These limitations make the solutions found in the prior art unsatisfactory for identifying cards held in-hand by a card player.
A selection of prior art is listed here, with a few brief notes concerning each:
    Chen, W-Y. & Chung, C.-H. (2010). Robust poker image recognition scheme in playing card machine using hotelling transform, dct and run-length techniques. Digital Signal Processing, 20(3), 769-779. Their approach requires vision of the entirety of each card, and each card must be isolated on an even background. The image processing pipeline used is quite fragile if non-card background or foreground elements interfere with card boundaries. These will prevent the post-Sobel binary closing operation from functioning as intended. Oblique views are not supported, the camera must be orthogonal to the card face. Rotation of cards is supported. The high computational complexity of the Hotelling transform makes it impractical for use within a responsive device with limited processing capability.    Hollinger, G., Ward, N., & Everbach, E. C. (2004). Introducing computers to blackjack: Implementation of a card recognition system using computer vision techniques. Colby College, Waterville. Their approach requires vision of the entirety of each card, and each card must be isolated on a black background. They comment on performance, “The system worked fairly well, but slight changes in card orientation or overall illumination could cause bad card identification”    Martins, P., Reis, L. P., & Teófilo, L. (2011). Poker vision: playing cards and chips identification based on image processing. In Pattern Recognition and Image Analysis (pp. 436-443).    Springer. The authors used two webcams, one to identify playing cards in the common area and another to identify the “hole cards”. They placed the hole cards face down on a transparent plate, and positioned the “pocket cam” under the table to see these hidden cards' faces. In each case, the entirety of each card must be visible, and the edges must not overlap. “This algorithm relies on the great contrast between the poker table and the cards lying on it . . . ”.    Zheng, C. & Green, R. (2007). Playing card recognition using rotational invariant template matching. In Proceedings of Image and Vision Computing New Zealand 2007, (pp. 276-281). Hamilton, New Zealand.            Presents a method to identify, rotate, scale, and identify a playing card within an image using template matching. They note that noise significantly impacts the accuracy, and that the approach can not be generalized to include cards with dissimilar faces.            Zutis, K. & Hoey, J. (2009). Who's counting? real-time blackjack monitoring for card counting detection. In Computer Vision Systems (pp. 354-363). Springer. Describes their system of monitoring blackjack games to identify card counting behavior. As part of their approach, cards are imaged and identified. The employ a SIFT algorithm face card recognition, and rely on pip counting to determine the rank of value cards. Their suit identification is poor, and unimportant for blackjack.            Other prior art included in the Information Disclosure Statement contains similar shortcomings.        
Many of the disclosed embodiments are concerned with providing assistance to blind individuals. Blind people who wish to play card games currently have few choices. They can play over the Internet on sites such as Blind Cafe.Net. They can purchase special braille playing cards, provided they can read braille. They can use a smartphone app such as Digit-Eyes to read cards specially marked with a Quick Response Code (QR code). Each of these options has drawbacks and limitations which a general purpose playing card reader would address.
One of the joys of playing card games is the act of sitting with friends and family, and enjoying the camaraderie and company offered in their presence. Playing on the computer with acquaintances over the Internet is nice, but it doesn't necessarily provide the same experience as a live game with one's grandchildren.
The American Printing House for the Blind, in their 2012 Annual Report, reported that only 8.8% of legally blind children enrolled in a US elementary or high school read braille. It is estimated that fewer than 10% of blind adults can read braille. For those without this skill a braille deck is not useful. For those who are braille literate this still requires the perpetual purchase of expensive specialty decks as their old decks wear out. Some players also report that the time required to read a hand of braille cards is cumbersome and disruptive to the pace of play.
Specially marked decks for use with QR code readers, bar code scanners, etc. also require expensive replacement decks. These decks are often not available with the high quality papers and finishes that are standard on premium mass-produced decks. Interaction with a smartphone, which requires touching virtual buttons on a tactilely-featureless screen, during game play is also distracting, and can be difficult for the blind.
While prior art devices are likely effective for their intended uses, they do not describe a capable general purpose playing card reader. A general purpose playing card reader would be capable of recognizing standard playing cards over a broad range of distances, with any rotation relative to the sensor, and within a broad range of inclination toward or away from the sensor along any axis. It would also be capable of recognizing multiple partially-overlapping cards simultaneously, each independently assuming any rotation, inclination, and distance relative to the sensor. Disclosed herein are embodiments and a method which are capable of these tasks.
Again, playing cards are discussed as the most widely known example of items uniquely identified by a pair of glyphs, and the one which contains relevant prior art. This disclosure should be interpreted to include the broader scope of all marking systems utilizing a pair of glyphs for unique identification.